遗传算法的板材排版C#代码

以下是一个简单的遗传算法实现板材排版的C#代码示例：

using System;
using System.Collections.Generic;

namespace BoardCuttingGA
{
    class Program
    {
        static void Main(string[] args)
        {
            // 初始化参数
            int populationSize = 20;
            int generations = 100;
            double mutationRate = 0.01;
            double crossoverRate = 0.7;
            int elitismCount = 2;

            // 定义板材和零件
            Board board = new Board(4, 8);
            List<Part> parts = new List<Part>()
            {
                new Part(2, 3),
                new Part(1, 4),
                new Part(3, 2),
                new Part(1, 2),
                new Part(2, 1)
            };

            // 创建遗传算法并运行
            GeneticAlgorithm ga = new GeneticAlgorithm(populationSize, board, parts, elitismCount, mutationRate, crossoverRate);
            ga.Run(generations);

            // 输出结果
            Console.WriteLine("Best solution found:");
            Console.WriteLine(ga.BestChromosome.ToString());
            Console.WriteLine("Fitness: " + ga.BestChromosome.Fitness);
            Console.ReadLine();
        }
    }

    // 板材类
    class Board
    {
        public int Width { get; set; }
        public int Height { get; set; }

        public Board(int width, int height)
        {
            Width = width;
            Height = height;
        }
    }

    // 零件类
    class Part
    {
        public int Width { get; set; }
        public int Height { get; set; }

        public Part(int width, int height)
        {
            Width = width;
            Height = height;
        }
    }

    // 布局类
    class Layout
    {
        public List<Part> Parts { get; set; }

        public Layout(List<Part> parts)
        {
            Parts = parts;
        }

        // 计算适应度
        public double CalculateFitness(Board board)
        {
            int remainingWidth = board.Width;
            int remainingHeight = board.Height;

            foreach (Part part in Parts)
            {
                if (part.Width > remainingWidth || part.Height > remainingHeight)
                    return 0;

                if (part.Width <= remainingWidth && part.Height <= remainingHeight)
                {
                    remainingWidth -= part.Width;
                    remainingHeight -= part.Height;
                }
            }

            return (double)(board.Width * board.Height - remainingWidth * remainingHeight);
        }

        // 转换为字符串
        public override string ToString()
        {
            string result = "";
            foreach (Part part in Parts)
            {
                result += "(" + part.Width + ", " + part.Height + ") ";
            }
            return result;
        }
    }

    // 染色体类
    class Chromosome
    {
        public List<Part> Parts { get; set; }
        public double Fitness { get; set; }

        public Chromosome(List<Part> parts, Board board)
        {
            Layout layout = new Layout(parts);
            Fitness = layout.CalculateFitness(board);
            Parts = parts;
        }

        // 转换为字符串
        public override string ToString()
        {
            return new Layout(Parts).ToString();
        }
    }

    // 遗传算法类
    class GeneticAlgorithm
    {
        private int populationSize;
        private Board board;
        private List<Part> parts;
        private int elitismCount;
        private double mutationRate;
        private double crossoverRate;
        private List<Chromosome> population;

        public Chromosome BestChromosome { get; private set; }

        public GeneticAlgorithm(int populationSize, Board board, List<Part> parts, int elitismCount, double mutationRate, double crossoverRate)
        {
            this.populationSize = populationSize;
            this.board = board;
            this.parts = parts;
            this.elitismCount = elitismCount;
            this.mutationRate = mutationRate;
            this.crossoverRate = crossoverRate;
            this.population = new List<Chromosome>();
        }

        // 初始化种群
        private void InitializePopulation()
        {
            for (int i = 0; i < populationSize; i++)
            {
                List<Part> randomParts = new List<Part>();
                foreach (Part part in parts)
                {
                    if (new Random().NextDouble() > 0.5)
                        randomParts.Add(new Part(part.Width, part.Height));
                }
                population.Add(new Chromosome(randomParts, board));
            }
        }

        // 计算适应度总和
        private double CalculateFitnessSum()
        {
            double sum = 0;
            foreach (Chromosome chromosome in population)
            {
                sum += chromosome.Fitness;
            }
            return sum;
        }

        // 选择操作
        private Chromosome Select()
        {
            double fitnessSum = CalculateFitnessSum();
            double rand = new Random().NextDouble() * fitnessSum;

            double runningSum = 0;
            foreach (Chromosome chromosome in population)
            {
                runningSum += chromosome.Fitness;
                if (runningSum > rand)
                    return chromosome;
            }

            return population[population.Count - 1];
        }

        // 交叉操作
        private Chromosome Crossover(Chromosome parent1, Chromosome parent2)
        {
            List<Part> childParts = new List<Part>();
            int crossoverPoint = new Random().Next(0, parts.Count - 1);

            for (int i = 0; i < crossoverPoint; i++)
            {
                childParts.Add(parent1.Parts[i]);
            }

            for (int i = crossoverPoint; i < parts.Count; i++)
            {
                childParts.Add(parent2.Parts[i]);
            }

            return new Chromosome(childParts, board);
        }

        // 变异操作
        private void Mutate(Chromosome chromosome)
        {
            foreach (Part part in chromosome.Parts)
            {
                if (new Random().NextDouble() < mutationRate)
                {
                    part.Width = Math.Max(1, part.Width + new Random().Next(-1, 2));
                    part.Height = Math.Max(1, part.Height + new Random().Next(-1, 2));
                }
            }
            chromosome.Fitness = new Layout(chromosome.Parts).CalculateFitness(board);
        }

        // 运行遗传算法
        public void Run(int generations)
        {
            InitializePopulation();

            for (int i = 0; i < generations; i++)
            {
                population.Sort((x, y) => y.Fitness.CompareTo(x.Fitness));

                BestChromosome = population[0];

                List<Chromosome> newPopulation = new List<Chromosome>();

                for (int j = 0; j < elitismCount; j++)
                {
                    newPopulation.Add(population[j]);
                }

                while (newPopulation.Count < populationSize)
                {
                    Chromosome parent1 = Select();
                    Chromosome parent2 = Select();

                    if (new Random().NextDouble() < crossoverRate)
                    {
                        Chromosome child = Crossover(parent1, parent2);
                        newPopulation.Add(child);
                    }
                    else
                    {
                        newPopulation.Add(parent1);
                    }
                }

                foreach (Chromosome chromosome in newPopulation)
                {
                    if (new Random().NextDouble() < mutationRate)
                    {
                        Mutate(chromosome);
                    }
                }

                population = newPopulation;
            }
        }
    }
}

在这个示例中，我们定义了一个 `Board` 类来表示板材，一个 `Part` 类来表示零件，以及一个 `Layout` 类来表示布局。我们还定义了一个 `Chromosome` 类来表示染色体，其中包含一个零件列表和一个适应度值。最后，我们创建了一个 `GeneticAlgorithm` 类来实现遗传算法。

在 `GeneticAlgorithm` 类中，我们首先使用 `InitializePopulation` 方法初始化种群。然后，在 `Run` 方法中，我们对种群进行进化。每一代，我们按适应度从高到低对种群进行排序，并选择最佳染色体作为当前最优解。然后，我们使用选择、交叉和变异操作生成新的染色体，并将它们加入到新的种群中。

在选择操作中，我们按适应度总和随机选择一个染色体。在交叉操作中，我们随机选择一个交叉点，并将两个父染色体的部分合并成一个新的子染色体。在变异操作中，我们随机选择一个零件，并随机增加或减少它的宽度或高度。

最后，我们输出找到的最佳解。